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Peer-reviewedCC BY 4.0Journal of Chemical Information and Modeling

An Inverse Design Workflow for Solvent Mixtures Based on Hansen Solubility Parameters

  • Xue Fang1,2
  • Shaohua Li1,3
  • Bo Gao4
  • Ella M. Gale1
  • Charl F. J. Faul1
  • Natalie Fey1
4 affiliations
  1. 1School of Chemistry, University of Bristol, Cantock’s Close, Bristol BS8 1TS, U.K.
  2. 2Molecular Sciences Research Hub, Imperial College London, White City Campus, 82 Wood Lane, London W12 0BZ, U.K.
  3. 3Department of Chemical Engineering, Imperial College London, South Kensington campus, London SW7 2AZ, U.K.
  4. 4School of Physics, HH Wills Physics Laboratory, University of Bristol, Tyndall Avenue, Bristol BS8 1TL, U.K.
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Abstract

We present SolvPred, an open-source Python framework for inverse design of solvent mixtures based on Hansen solubility parameters (HSPs). SolvPred allows users to control the composition of solvent mixtures while setting essential experimental constraints. As a case study, we applied SolvPred to optimize a polyimide condensation as a model reaction, where the hydrogen-bonding HSP term was selectively tuned. SolvPred identified seven viable ternary mixtures from an otherwise intractable space of 171,871 possibilities. The best-performing solvent mixture achieved a fivefold improvement in the product yield at a milder temperature when compared with the conventional single-solvent condition. This work establishes SolvPred as a practical solution for rational solvent mixture design and experimental planning, as well as an efficient tool for property-guided solvent optimization within data-driven experimental workflows.

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Cite

BibTeXRIS
BibTeX
@article{fang2026inverse,
  title = {{An Inverse Design Workflow for Solvent Mixtures Based on Hansen Solubility Parameters}},
  author = {Xue Fang and Shaohua Li and Bo Gao and Ella M. Gale and Charl F. J. Faul and Natalie Fey},
  journal = {Journal of Chemical Information and Modeling},
  year = {2026},
  month = oct,
  doi = {10.1021/acs.jcim.6c02092},
  url = {https://doi.org/10.1021/acs.jcim.6c02092},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - An Inverse Design Workflow for Solvent Mixtures Based on Hansen Solubility Parameters
AU  - Xue Fang
AU  - Shaohua Li
AU  - Bo Gao
AU  - Ella M. Gale
AU  - Charl F. J. Faul
AU  - Natalie Fey
PY  - 2026
DA  - 2026/10/02
JO  - Journal of Chemical Information and Modeling
DO  - 10.1021/acs.jcim.6c02092
UR  - https://doi.org/10.1021/acs.jcim.6c02092
AB  - We present SolvPred, an open-source Python framework for inverse design of solvent mixtures based on Hansen solubility parameters (HSPs). SolvPred allows users to control the composition of solvent mixtures while setting essential experimental constraints. As a case study, we applied SolvPred to optimize a polyimide condensation as a model reaction, where the hydrogen-bonding HSP term was selectively tuned. SolvPred identified seven viable ternary mixtures from an otherwise intractable space of 171,871 possibilities. The best-performing solvent mixture achieved a fivefold improvement in the product yield at a milder temperature when compared with the conventional single-solvent condition. This work establishes SolvPred as a practical solution for rational solvent mixture design and experimental planning, as well as an efficient tool for property-guided solvent optimization within data-driven experimental workflows.
SN  - 1549-9596
SN  - 1549-960X
ER  -